TABLE 3.
GWAS Method | Chromosome | Peak SNP position | Co-located QTL | Environmenta | References |
| |||||
MLM | 3 | 22283184 | NA | ||
22283218 | NA | ||||
22283256 | NA | ||||
44326068 | NA | ||||
|
|||||
7 | 20220381 | NA | |||
20280812 | NA | ||||
23349322 | NA | ||||
|
|||||
18 | 56952847 | pod number 4-g8 | NA | Hao et al., 2012 | |
plant height 3-g14 | NA | Contreras-Soto et al., 2017 | |||
56952858 | shoot K 1-g39 | NA | Dhanapal et al., 2018 | ||
WUE 3-g32 | NA | Dhanapal et al., 2018 | |||
| |||||
FarmCPU | 3 | 22283184 | NA | ||
22283218 | NA | ||||
22283256 | NA | ||||
44326068 | NA | ||||
|
|||||
7 | 20220330 | Shoot Mn 1-g2 | NA | Dhanapal et al., 2018 | |
20220381 | NA | ||||
20280812 | NA | ||||
23317163 | Ureide content 1-g9 | NA | Ray et al., 2015 | ||
23349322 | shoot Mn 1-g3 | NA | Dhanapal et al., 2018 | ||
27492738 | NA | ||||
|
|||||
18 | 56952847 | pod number 4-g8 | NA | Hao et al., 2012 | |
plant height 3-g14 | NA | Contreras-Soto et al., 2017 | |||
56952858 | shoot K 1-g39 | NA | Dhanapal et al., 2018 | ||
WUE 3-g32 | NA | Dhanapal et al., 2018 | |||
57794992 | NA | ||||
| |||||
SVR | 14 | 16425108 | NA | ||
|
|||||
15 | 13118545 | NA | |||
12895268 | NA | ||||
12894320 | NA | ||||
14174744 | NA | ||||
14378690 | 1 | ||||
12892003 | seed coat color 3-g3 | NA | Vuong et al., 2015 | ||
seed yield, soyNAM 7-g14 | NA | Diers et al., 2018 | |||
18302021 | NA | ||||
14406716 | 1 | ||||
17877705 | NA | ||||
17362699 | NA | ||||
14088239 | 1 | ||||
13163194 | SCN 5-g33 | NA | Li et al., 2016 | ||
First flower 4-g58 | NA | Mao et al., 2017 | |||
First Flower 5-g29.1 | NA | Fang et al., 2017 | |||
First flower 5-g29.2 | NA | Fang et al., 2017 | |||
First flower 5-g29.3 | NA | Fang et al., 2017 | |||
14421366 | 1 | ||||
|
|||||
19 | 36064225 | 1 |
aDetected in separate environments in addition to the combined environment. (1) 2018Ridgetown, (2) 2019Ridgetwon, (3) 2018Palmyra, (4) 2019Palmyra, (NA) not found in any separate environment.
MLM, mixed linear model; FarmCPU, fixed and random model circulating probability unification; RF, random forest; SVR, support vector regression.